gemma-2-9b-it-abliterated-GGUF
| Property | Value |
|---|---|
| Original Model | IlyaGusev/gemma-2-9b-it-abliterated |
| Publisher | bartowski |
| Quantization Framework | llama.cpp (b3878) |
| Format | GGUF with imatrix quantizations |
What is gemma-2-9b-it-abliterated-GGUF?
This is a comprehensive quantization suite of the Gemma 2B model, offering various compression levels to accommodate different hardware configurations and performance requirements. The model provides multiple GGUF formats ranging from full F32 weights (36.97GB) down to highly compressed IQ2_M (3.43GB) variants.
Implementation Details
The implementation utilizes llama.cpp's imatrix quantization technology, offering a spectrum of compression options. Each variant is optimized for specific use cases, from maximum quality (Q8_0) to balanced performance (Q4_K_M) to minimum size requirements (IQ2_M).
- Multiple quantization formats including Q8_0, Q6_K, Q5_K, Q4_K, and IQ series
- Special ARM-optimized variants (Q4_0_X_X series)
- Enhanced embed/output weight options for improved quality
- Comprehensive size options from 36.97GB to 3.43GB
Core Capabilities
- Flexible deployment across different hardware configurations
- Optimized performance for both CPU and GPU inference
- Special optimization for ARM chips with different instruction set support
- Quality-size tradeoff options for various use cases
Frequently Asked Questions
Q: What makes this model unique?
The model offers an exceptionally wide range of quantization options, allowing users to find the perfect balance between model size, inference speed, and output quality. It includes modern I-quants and specialized ARM optimizations.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L or Q8_0 variants. For balanced performance, Q4_K_M is recommended. For limited RAM scenarios, the IQ3/IQ2 series provides surprisingly usable performance at minimal size requirements.
